Unsupervised Nonlinear Feature Extraction Method and Its Effects on Target Detection in High-dimensional Data

The principal component analysis (PCA) is one of the most effective unsupervised techniques for feature extraction. To extract higher order properties of data, researchers extended PCA to kernel PCA (KPCA) by means of kernel machines. In this paper, KPCA is applied as a feature extraction procedure to dimension reduction for target detection as a preprocessing on hyperspectral images. Then the detection was done with a support vector data description (SVDD) algorithm which is another type of one-class support vector machines (SVMs). The SVDD constructs a minimum hypersphere enclosing the target objects as much as possible. For the supervised learning, SVDD has been trained with a training set which has been chosen from target class. Balanced classification rate (BCR) and F-measure have been used to evaluate the performance of proposed technique against full-band target detection. The experimental results on hyperspectral data from the HYDICE sensors show that the KPCA based dimension reduction offers high performance for target detection applications by SVDD.